research-article

Understanding Privacy Risks and Perceived Benefits in Open Dataset Collection for Mobile Affective Computing

Authors:

Uichin LeeAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 6, Issue 2

Article No.: 61, Pages 1 - 26

https://doi.org/10.1145/3534623

Published: 07 July 2022 Publication History

Abstract

Collecting large-scale mobile and wearable sensor datasets from daily contexts is essential in developing machine learning models for enabling everyday affective computing applications. However, there is a lack of knowledge on data contributors' perceived benefits and risks in participating in open dataset collection projects. To bridge this gap, we conducted an in-situ study on building an open dataset with mobile and wearable devices for affective computing research (N = 100, 4 weeks). Our study results showed that a mixture of financial and altruistic benefits was important in eliciting data contribution. Sensor-specific risks were largely associated with the revelation of personal traits and social behaviors. However, most of the participants were less concerned with open dataset collection and their perceived sensitivity of each sensor data did not change over time. We further discuss alternative approaches to promote data contributors' motivations and suggest design guidelines to alleviate potential privacy concerns in mobile open dataset collection.

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Fan LZhang SKong YYi XWang YXu XYu CLi HShi Y(2024)Evaluating the Privacy Valuation of Personal Data on SmartphonesProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36785098:3(1-33)Online publication date: 9-Sep-2024
https://dl.acm.org/doi/10.1145/3678509
Salehzadeh Niksirat KVelykoivanenko LZufferey NCherubini MHuguenin KHumbert M(2024)Wearable Activity Trackers: A Survey on Utility, Privacy, and SecurityACM Computing Surveys10.1145/364509156:7(1-40)Online publication date: 9-Apr-2024
https://dl.acm.org/doi/10.1145/3645091
Lee HJung YShin YPark HChoi WLee U(2024)FamilyScope: Visualizing Affective Aspects of Family Social Interactions using Passive Sensor DataProceedings of the ACM on Human-Computer Interaction10.1145/36373348:CSCW1(1-27)Online publication date: 26-Apr-2024
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Index Terms

Understanding Privacy Risks and Perceived Benefits in Open Dataset Collection for Mobile Affective Computing
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Empirical studies in HCI
  2. Ubiquitous and mobile computing
    1. Ubiquitous and mobile devices
      1. Mobile devices

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Mobile networks have become ubiquitous and the primary means to access the Internet, and the traffic they generate has rapidly increased over the last years. The technology and service diversity in mobile networks call for extensive and accurate ...
Understanding identity exposure in pervasive computing environments

Various miniaturized computing devices that store our identity information are emerging rapidly and are likely to become ubiquitous in the future. They allow private information to be exposed and accessed easily via wireless networks. When identity and ...

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 6, Issue 2

July 2022

1551 pages

EISSN:2474-9567

DOI:10.1145/3547347

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Copyright © 2022 ACM.

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Published: 07 July 2022

Published in IMWUT Volume 6, Issue 2

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Basic Science Research Program through the National Research Foundation (NRF) funded by the Korean government (MSIT)
Bio & Medical Technology Development Program of the National Research Foundation (NRF) funded by the Korean government (MSIT)

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Fan LZhang SKong YYi XWang YXu XYu CLi HShi Y(2024)Evaluating the Privacy Valuation of Personal Data on SmartphonesProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36785098:3(1-33)Online publication date: 9-Sep-2024
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Salehzadeh Niksirat KVelykoivanenko LZufferey NCherubini MHuguenin KHumbert M(2024)Wearable Activity Trackers: A Survey on Utility, Privacy, and SecurityACM Computing Surveys10.1145/364509156:7(1-40)Online publication date: 9-Apr-2024
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Bolpagni MPardini SGabrielli S(2024)Human centered design of AI-powered Digital Therapeutics for stress prevention: Perspectives from multi-stakeholders' workshops about the SHIVA solutionInternet Interventions10.1016/j.invent.2024.10077538(100775)Online publication date: Dec-2024
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Sankar JDavid AValan P(2023)Examining User Understanding and Perceptions of E-Commerce Data Privacy, Security, and ProtectionConfronting Security and Privacy Challenges in Digital Marketing10.4018/978-1-6684-8958-1.ch008(159-185)Online publication date: 30-Jun-2023
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Kalupahana ABalaji AXiao XPeh L(2023)SeRaNDiPProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35962527:2(1-38)Online publication date: 12-Jun-2023
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Feng JZhao TSarkar SKonrad DJacques TCabric DSehatbakhsh N(2023)Fingerprinting IoT Devices Using Latent Physical Side-ChannelsProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35962477:2(1-26)Online publication date: 12-Jun-2023
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Welch KPennington RVanaparthy SDo HNarayanan RPopa DBarnes GKuravackel G(2023)Using Physiological Signals and Machine Learning Algorithms to Measure Attentiveness During Robot-Assisted Social Skills Intervention: A Case Study of Two Children with Autism Spectrum DisorderIEEE Instrumentation & Measurement Magazine10.1109/MIM.2023.1012141226:3(39-45)Online publication date: May-2023
https://doi.org/10.1109/MIM.2023.10121412
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